Abstract
The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables) at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.
Highlights
Nowadays, there are various types of modern optimization techniques, such as Evolutionary Programming (EP), Artificial Immune System (AIS), Ant Colony Optimization (ACO), and Artificial Bees Colony (ABC)
This paper proposed a new algorithm for Particle Swarm Optimization, which is known as Global Particle Swarm Optimization (GPSO), to determine the global optimal value for high dimensional optimization problems
The performance of GPSO is tested on 12 classical benchmark high dimensional test functions and compared with 3 other PSO methods, which are Original Particle Swarm Optimization (PSO), Evolutionary Particle Swarm Optimization (EPSO), and Iteration Particle Swarm Optimization (IPSO)
Summary
There are various types of modern optimization techniques, such as Evolutionary Programming (EP), Artificial Immune System (AIS), Ant Colony Optimization (ACO), and Artificial Bees Colony (ABC). All of them can be regarded as heuristic optimization methods, due to the randomization involved in their respective initial steps. Despite the usage of randomized values, the mutation process and other steps in the algorithm render the optimization method capable of solving both linear and nonlinear problems. It can be seen that optimization techniques are applicable in many fields. There is no specific optimization algorithm that can reach a global solution for every optimization problem Among the optimization methods, PSO has become very popular, due to its simplicity and its affinity towards manufacturing and robotics [1,2,3], electrical power systems [4,5,6,7,8], and engineering [9,10,11] and in other areas [12,13,14,15,16,17,18].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
